Determination of hydraulic conductivity and soil-water characteristic curve in different soil textures by mathematical equations

Measuring soil hydraulic properties is essential in soil science, but direct measurements are often laborious, time-consuming, and costly. Therefore, alternative methods based on mathematical models have gained increasing attention. This study aimed to estimate saturated hydraulic conductivity (Ks) and soil water characteristic curves (SWCC) for soils with different textures using various predictive models. Soil samples were collected from the Arpacı, Çanakçı, and Baharlı soil series in Adana, Türkiye. Twelve undisturbed and one disturbed sample were obtained from the 0–20 cm depth. Soil physical and chemical properties, including bulk density, porosity, pH, electrical conductivity, CaCO₃ content, organic matter, hydraulic conductivity, and pF curves, were determined. Soil water retention was measured at 0, 0.1, 0.2, 0.33, 0.5, 1, 5, and 15 atm pressure levels. Model selection for SWCC and hydraulic conductivity estimations was performed using the pFK model database integrated into the SIMONA program. Model performance was evaluated using Root Mean Square Error (RMSE). For the Arpacı series, the Terleev and Sariyev model provided the best fit for SWCC and K(P) estimations (RMSE = 0.007453), whereas the van Genuchten model performed best for K(W) (RMSE = 0.041119). In the Çanakçı series, the Curry and Chen model showed the highest accuracy for SWCC prediction (RMSE = 0.003780), while the Terleev and Sariyev model was most suitable for K(P) (RMSE = 0.146915) and the van Genuchten model for K(W) (RMSE = 0.293829). For the Baharlı series, the Brooks and Corey model provided the best SWCC estimation (RMSE = 0.013715), whereas the Michurin model was most accurate for K(P) (RMSE = 0.000014) and the Avern model for K(W) (RMSE = 0.482353). The results indicate that selecting texture-specific models can improve the estimation of soil hydraulic properties and contribute to more accurate irrigation scheduling and water management practices.